### Filter Potentiometer Noise in C++

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

A complete example demonstrating how to filter noisy analog readings from a potentiometer. It adds synthetic noise to a signal and uses the Kalman filter to recover the original value.

```cpp
#include <SimpleKalmanFilter.h>

SimpleKalmanFilter simpleKalmanFilter(2, 2, 0.01);

const long SERIAL_REFRESH_TIME = 100;
long refresh_time;

void setup() {
  Serial.begin(115200);
}

void loop() {
  float real_value = analogRead(A0) / 1024.0 * 100.0;
  float measured_value = real_value + random(-100, 100) / 100.0;
  float estimated_value = simpleKalmanFilter.updateEstimate(measured_value);

  if (millis() > refresh_time) {
    Serial.print(real_value, 4);
    Serial.print(",");
    Serial.print(measured_value, 4);
    Serial.print(",");
    Serial.print(estimated_value, 4);
    Serial.println();

    refresh_time = millis() + SERIAL_REFRESH_TIME;
  }
}
```

--------------------------------

### Update Kalman Filter Estimate with New Measurement (Arduino)

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Shows the core usage of the SimpleKalmanFilter library, where a new sensor reading is processed using the updateEstimate() method. The example reads an analog value, filters it, and then prints both the raw and filtered values to the serial monitor, demonstrating noise reduction.

```cpp
#include <SimpleKalmanFilter.h>

SimpleKalmanFilter kf(2, 2, 0.01);

void setup() {
  Serial.begin(115200);
}

void loop() {
  // Read raw sensor value
  float raw_value = analogRead(A0) / 1024.0 * 100.0;

  // Get filtered estimate
  float filtered_value = kf.updateEstimate(raw_value);

  // Output: raw noisy value vs smooth filtered value
  Serial.print("Raw: ");
  Serial.print(raw_value);
  Serial.print(" | Filtered: ");
  Serial.println(filtered_value);

  delay(100);
}
```

--------------------------------

### Smooth Altitude Estimation with BMP180 in C++

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Integrates the Kalman filter with a BMP180 barometric pressure sensor. This example shows how to produce stable altitude readings by filtering noisy pressure data.

```cpp
#include <SimpleKalmanFilter.h>
#include <SFE_BMP180.h>

SimpleKalmanFilter pressureKalmanFilter(1, 1, 0.01);
SFE_BMP180 pressure;
float baseline;
const long SERIAL_REFRESH_TIME = 100;
long refresh_time;

double getPressure() {
  char status;
  double T, P;
  status = pressure.startTemperature();
  if (status != 0) {
    delay(status);
    status = pressure.getTemperature(T);
    if (status != 0) {
      status = pressure.startPressure(3);
      if (status != 0) {
        delay(status);
        status = pressure.getPressure(P, T);
        if (status != 0) return P;
      }
    }
  }
  return 0;
}

void setup() {
  Serial.begin(115200);
  if (!pressure.begin()) while(1);
  baseline = getPressure();
}

void loop() {
  float p = getPressure();
  float altitude = pressure.altitude(p, baseline);
  float estimated_altitude = pressureKalmanFilter.updateEstimate(altitude);

  if (millis() > refresh_time) {
    Serial.print(altitude, 6);
    Serial.print(",");
    Serial.print(estimated_altitude, 6);
    Serial.println();
    refresh_time = millis() + SERIAL_REFRESH_TIME;
  }
}
```

--------------------------------

### Initialize Simple Kalman Filter Instances (Arduino)

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Demonstrates how to create and initialize SimpleKalmanFilter objects with different parameters for measurement uncertainty, estimation uncertainty, and process noise. This allows tuning the filter for various signal characteristics, such as fast-changing or slow-changing data streams.

```cpp
#include <SimpleKalmanFilter.h>

// Create filter with measurement uncertainty=2, estimation uncertainty=2, process noise=0.01
SimpleKalmanFilter temperatureFilter(2, 2, 0.01);

// For fast-changing signals, use higher process noise
SimpleKalmanFilter accelerometerFilter(1, 1, 0.1);

// For slow-changing signals, use lower process noise
SimpleKalmanFilter pressureFilter(1, 1, 0.001);
```

--------------------------------

### Dynamically Adjust Process Noise (Arduino)

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Shows how to modify the process noise parameter (q) using setProcessNoise() during runtime. This allows switching between modes for smooth filtering of stable signals or faster tracking of rapidly changing signals.

```cpp
#include <SimpleKalmanFilter.h>

SimpleKalmanFilter kf(2, 2, 0.01);
bool fastMode = false;

void setup() {
  Serial.begin(115200);
  pinMode(2, INPUT_PULLUP);  // Mode switch button
}

void loop() {
  // Toggle between fast and slow tracking modes
  if (digitalRead(2) == LOW) {
    fastMode = !fastMode;
    if (fastMode) {
      kf.setProcessNoise(0.1);   // Fast tracking mode
      Serial.println("Fast tracking enabled");
    } else {
      kf.setProcessNoise(0.01);  // Smooth filtering mode
      Serial.println("Smooth filtering enabled");
    }
    delay(300);  // Debounce
  }

  float estimate = kf.updateEstimate(analogRead(A0));
  Serial.println(estimate);
  delay(50);
}
```

--------------------------------

### Initialize and Update Simple Kalman Filter

Source: https://github.com/denyssene/simplekalmanfilter/blob/master/README.md

Demonstrates how to instantiate the SimpleKalmanFilter class and update the estimate with new sensor readings in an Arduino loop. The updateEstimate method takes a raw sensor value and returns the filtered estimate.

```c++
SimpleKalmanFilter kf = SimpleKalmanFilter(e_mea, e_est, q);

while (1) {
  float x = analogRead(A0);
  float estimated_x = kf.updateEstimate(x);
  
  // ...
}
```

--------------------------------

### Retrieve Kalman Gain in C++

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Demonstrates how to fetch the current Kalman gain using getKalmanGain(). This value indicates the filter's trust in new measurements versus predictions, ranging from 0 to 1.

```cpp
#include <SimpleKalmanFilter.h>

SimpleKalmanFilter kf(2, 2, 0.01);

void setup() {
  Serial.begin(115200);
}

void loop() {
  float measurement = analogRead(A0);
  float estimate = kf.updateEstimate(measurement);
  float gain = kf.getKalmanGain();

  Serial.print("Measurement: ");
  Serial.print(measurement);
  Serial.print(" | Estimate: ");
  Serial.print(estimate);
  Serial.print(" | Kalman Gain: ");
  Serial.println(gain);

  delay(100);
}
```

--------------------------------

### Set Kalman Filter Estimation Error (Arduino)

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Demonstrates the use of the setEstimateError() method to configure the estimation uncertainty parameter. This setting influences how quickly the filter adapts to new measurements, with higher values allowing for faster response.

```cpp
#include <SimpleKalmanFilter.h>

SimpleKalmanFilter kf(2, 2, 0.01);

void setup() {
  Serial.begin(115200);

  // Reset estimation error to high value when starting fresh
  // This makes the filter quickly adapt to initial measurements
  kf.setEstimateError(10);
}

void loop() {
  float measurement = analogRead(A0);
  float estimate = kf.updateEstimate(measurement);

  Serial.print("Measurement: ");
  Serial.print(measurement);
  Serial.print(" | Estimate: ");
  Serial.println(estimate);
  delay(100);
}
```

--------------------------------

### Retrieve Estimation Error in C++

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Shows how to monitor the filter's confidence using getEstimateError(). Lower values indicate higher confidence in the current estimate, which is useful for debugging convergence.

```cpp
#include <SimpleKalmanFilter.h>

SimpleKalmanFilter kf(2, 2, 0.01);

void setup() {
  Serial.begin(115200);
}

void loop() {
  float measurement = analogRead(A0);
  float estimate = kf.updateEstimate(measurement);
  float error = kf.getEstimateError();

  Serial.print("Estimate: ");
  Serial.print(estimate);
  Serial.print(" | Confidence Error: ");
  Serial.println(error);

  delay(100);
}
```

--------------------------------

### Dynamically Adjust Measurement Error (Arduino)

Source: https://context7.com/denyssene/simplekalmanfilter/llms.txt

Illustrates how to use the setMeasurementError() method to dynamically adjust the measurement uncertainty parameter at runtime. This is useful for adapting the filter's trust in sensor readings based on changing conditions, such as signal strength.

```cpp
#include <SimpleKalmanFilter.h>

SimpleKalmanFilter kf(2, 2, 0.01);

void setup() {
  Serial.begin(115200);
}

void loop() {
  float measurement = analogRead(A0);

  // Increase measurement error when signal is weak (low values)
  if (measurement < 100) {
    kf.setMeasurementError(5);  // Less trust in measurements
  } else {
    kf.setMeasurementError(2);  // Normal trust level
  }

  float estimate = kf.updateEstimate(measurement);
  Serial.println(estimate);
  delay(50);
}
```

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